# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional

from ..._utils import pad_vocab_size
from ...functional import Tensor, recv, send
from ...layers import (Attention, AttentionMaskType, ColumnLinear, Embedding,
                       GatedMLP, PositionEmbeddingType, RmsNorm)
from ...mapping import Mapping
from ...module import Module
from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
                              PretrainedConfig, QuantConfig)
from .weight import load_from_hf_gemma


class GemmaDecoderLayer(Module):

    def __init__(self, config: PretrainedConfig, layer_idx: int):
        super().__init__()
        self.layer_idx = layer_idx
        self.config = config

        self.input_layernorm = RmsNorm(normalized_shape=config.hidden_size,
                                       eps=config.norm_epsilon,
                                       dtype=config.dtype)

        layers_range = config.mapping.pp_layers(config.num_hidden_layers)
        local_layer_idx = layer_idx - layers_range[0]
        self.attention = Attention(
            local_layer_idx=local_layer_idx,
            hidden_size=config.hidden_size,
            num_attention_heads=config.num_attention_heads,
            num_kv_heads=config.num_key_value_heads,
            attention_head_size=config.head_size,
            max_position_embeddings=config.max_position_embeddings,
            dtype=config.dtype,
            attention_mask_type=AttentionMaskType.causal,
            bias=config.attn_bias,
            position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
            rotary_embedding_base=config.rotary_base,
            rotary_embedding_scaling=config.rotary_scaling,
            tp_group=config.mapping.tp_group,
            tp_size=config.mapping.tp_size,
            quant_mode=config.quant_mode,
        )

        mlp_hidden_size = config.hidden_size * 4 if config.intermediate_size is None else config.intermediate_size

        self.mlp = GatedMLP(hidden_size=config.hidden_size,
                            ffn_hidden_size=mlp_hidden_size,
                            hidden_act=config.hidden_act,
                            dtype=config.dtype,
                            bias=config.mlp_bias,
                            tp_group=config.mapping.tp_group,
                            tp_size=config.mapping.tp_size,
                            quant_mode=config.quant_mode)
        self.post_layernorm = RmsNorm(normalized_shape=config.hidden_size,
                                      eps=config.norm_epsilon,
                                      dtype=config.dtype)

    def forward(
            self,
            hidden_states,
            attention_mask=None,
            medusa_packed_mask=None,  # For Medusa support
            medusa_position_offsets=None,
            use_cache=False,
            kv_cache_params=None,
            attention_params=None,
            lora_layer_params=None):
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)

        attention_output = self.attention(
            hidden_states,
            attention_mask=attention_mask,
            medusa_packed_mask=medusa_packed_mask,  # For Medusa support
            medusa_position_offsets=medusa_position_offsets,
            use_cache=use_cache,
            kv_cache_params=kv_cache_params,
            attention_params=attention_params,
            lora_layer_params=lora_layer_params)

        if use_cache:
            attention_output, presents = attention_output

        hidden_states = residual + attention_output

        residual = hidden_states
        hidden_states = self.post_layernorm(hidden_states)

        hidden_states = self.mlp(hidden_states,
                                 lora_layer_params=lora_layer_params)

        hidden_states = residual + hidden_states
        if use_cache:
            return (hidden_states, presents)
        return hidden_states


class GemmaModel(Module):

    def __init__(self, config: PretrainedConfig) -> None:
        super().__init__()

        self.mapping = config.mapping
        if self.mapping.is_first_pp_rank():
            self.vocab_embedding = Embedding(config.vocab_size,
                                             config.hidden_size,
                                             dtype=config.dtype)

        self.layers = DecoderLayerList(GemmaDecoderLayer, config)

        if self.mapping.is_last_pp_rank():
            self.ln_f = RmsNorm(normalized_shape=config.hidden_size,
                                eps=config.norm_epsilon,
                                dtype=config.dtype)

    def forward(self,
                input_ids,
                position_ids=None,
                use_cache=False,
                attention_mask=None,
                kv_cache_params=None,
                attention_params=None,
                hidden_states=None,
                prompt_embedding_table: Optional[Tensor] = None,
                prompt_tasks: Optional[Tensor] = None,
                prompt_vocab_size: Optional[Tensor] = None,
                lora_params=None):

        ptuning_args = [
            prompt_embedding_table, prompt_tasks, prompt_vocab_size
        ] if prompt_embedding_table is not None else []

        if self.mapping.is_first_pp_rank():
            hidden_states = self.vocab_embedding(input_ids, *ptuning_args)
        else:
            hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())

        hidden_states = self.layers.forward(
            hidden_states,
            use_cache=use_cache,
            attention_mask=attention_mask,
            kv_cache_params=kv_cache_params,
            attention_params=attention_params,
            lora_params=lora_params,
        )

        if use_cache:
            hidden_states, presents = hidden_states

        if self.mapping.is_last_pp_rank():
            hidden_states = self.ln_f(hidden_states)
        else:
            hidden_states = send(hidden_states, self.mapping.next_pp_rank())

        if use_cache:
            return (hidden_states, tuple(presents))
        return hidden_states


class GemmaForCausalLM(DecoderModelForCausalLM):

    def __init__(self, config: PretrainedConfig):

        self.check_config(config)
        transformer = GemmaModel(config)

        vocab_size_padded = pad_vocab_size(config.vocab_size,
                                           config.mapping.tp_size)
        if config.mapping.is_last_pp_rank():
            lm_head = ColumnLinear(config.hidden_size,
                                   vocab_size_padded,
                                   bias=False,
                                   dtype=config.dtype,
                                   tp_group=config.mapping.tp_group,
                                   tp_size=config.mapping.tp_size,
                                   gather_output=True)
        else:
            lm_head = None
        self.quant_mode = config.quant_mode
        self.mapping = config.mapping

        super().__init__(config, transformer, lm_head)

    @classmethod
    def from_hugging_face(cls,
                          hf_model_dir,
                          dtype='float16',
                          mapping: Optional[Mapping] = None,
                          **kwargs):
        import transformers
        from transformers import GemmaConfig

        from ...models.modeling_utils import PretrainedConfig
        cfg = GemmaConfig.from_pretrained(hf_model_dir)

        num_kv_heads = cfg.num_key_value_heads if hasattr(cfg, "num_key_value_heads") \
            else cfg.num_attention_heads
        quantization = kwargs.get('quantization', QuantConfig())
        if mapping is None:
            mapping = Mapping()

        cfg.mapping = mapping
        cfg.dtype = dtype
        cfg.norm_epsilon = cfg.rms_norm_eps

        config = {
            'architecture': cfg.architectures[0],
            'dtype': cfg.dtype,
            'logits_dtype': 'float32',
            'num_hidden_layers': cfg.num_hidden_layers,
            'num_attention_heads': cfg.num_attention_heads,
            'head_size': cfg.head_dim,
            'hidden_size': cfg.hidden_size,
            'intermediate_size': cfg.intermediate_size,
            'num_key_value_heads': num_kv_heads,
            'vocab_size': cfg.vocab_size,
            'position_embedding_type': 'rope_gpt_neox',
            'max_position_embeddings': cfg.max_position_embeddings,
            'hidden_act': cfg.hidden_act,
            'rotary_base': getattr(cfg, 'rotary_base', 10000.0),
            'rotary_scaling': getattr(cfg, 'rotary_scaling', None),
            'norm_epsilon': cfg.rms_norm_eps,
            'quantization': quantization.asdict(),
            'mapping': {
                'world_size': mapping.world_size,
                'tp_size': mapping.world_size,
            },
            'use_parallel_embedding': kwargs.get("use_parallel_embedding",
                                                 False),
            'embedding_sharding_dim': kwargs.get("embedding_sharding_dim", 0),
            'use_fused_mlp': kwargs.get("use_fused_mlp", False),
        }

        assert not quantization.quant_mode.has_any_quant()

        tllm_llama = GemmaForCausalLM(PretrainedConfig.from_dict(config))

        hf_model = transformers.GemmaForCausalLM
        hf_llama = hf_model.from_pretrained(
            hf_model_dir,
            device_map={
                "model": "cpu",
                "lm_head": "cpu",
                "embed_tokens": "cpu",
                "layers": "cpu",
                "norm": "cpu",
            },  # Load to CPU memory
            torch_dtype='auto',
        )

        weights = load_from_hf_gemma(
            tllm_llama,
            hf_llama,
            mapping=mapping,
            dtype=dtype,
            # TODO: these shall be outside from_hugging_face too.
            use_gemm_woq_plugin=kwargs.get("use_gemm_woq_plugin", False),
        )
        del hf_llama
        tllm_llama.load(weights)
        return tllm_llama

    def check_config(self, config):
        config.set_if_not_exist('use_parallel_embedding', False)
        config.set_if_not_exist('embedding_sharding_dim', 0)
        config.set_if_not_exist('mlp_bias', False)
        config.set_if_not_exist('attn_bias', False)
        config.set_if_not_exist('rotary_base', 10000.0)
        config.set_if_not_exist('rotary_scaling', None)
        config.set_if_not_exist('use_fused_mlp', False)
